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by Kenichi Kanatani
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ISBN: 0486443086 Dover Publications Price: $26.95 |
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This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise — a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition.
Slightly corrected republication of the edition published by Elsevier Science, Amsterdam, 1996.
Table of Contents for Statistical Optimization for Geometric Computation: Theory and Practice
| 1. Introduction | |||||||
| 2. Fundamentals of Linear Algebra | |||||||
| 3. Probabilities and Statistical Estimation | |||||||
| 4. Representation of Geometric Objects | |||||||
| 5. Geometric Correction | |||||||
| 6. 3-D Computation by Stereo Vision | |||||||
| 7. Parametric Fitting | |||||||
| 8. Optimal Filter | |||||||
| 9. Renormalization | |||||||
| 10. Applications of Geometric Estimation | |||||||
| 11. 3-D Motion Analysis | |||||||
| 12. 3-D Interpretation of Optical Flow | |||||||
| 13. Information Criterion for Model Selection | |||||||
| 14. General Theory of Geometric Estimation | |||||||
| References | |||||||
| Index |